This review synthesizes the evolution of aquatic ecosystem health assessment and proposes a paradigm shift from descriptive monitoring to predictive, intelligent governance. Led by Yong Liu from Peking University, the research team introduces a gene-to-landscape framework that integrates multi-omics, environmental DNA, ecological observation, and AI-enabled remote sensing to enable early warning, causal attribution, and anticipatory decision support for freshwater ecosystems under global change.

This review summarizes flexible electromagnetic induction-type tactile sensors that convert touch-induced magnetic-flux variation into electrical signals. It establishes an integrated framework linking magnets, coils, soft materials, signal processing and circuits, and discusses their relevance to wearable electronics, medical monitoring, human-machine interfaces and intelligent robotics.

The alliance lays the foundation for a broad, multi-target collaboration framework, subject to the parties’ further discussion and execution of definitive agreements. If fully implemented, the potential value of the proposed collaboration could exceed US$2.5 billion.  Insilico expects to support Bora in strengthening AI capabilities across its global workforce and enhancing AI literacy across the organization.  The partnership is also expected to apply Insilico’s AI capabilities to improve efficiency across manufacturing, supply chain, distribution, and corporate operations. As the collaboration progresses, Bora and Insilico expect to further refine its scope, scale, and operating framework.

A research team led by Professor Changick Kim from the School of Electrical Engineering has developed “Buffer-and-Reinforce,” a framework that preserves AI safety even after personalized fine-tuning.

The framework achieves both personalized performance and safety — a key challenge in the era of AI personalization — and the study was selected as a Spotlight paper at ICML 2026, placing it among the top 2.2% of submissions.